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NASA/TM–2018-219027/ Vol. 1
February 2018
National Aeronautics and Space Administration
Goddard Space Flight Center Greenbelt, Maryland 20771
PACE Technical Report Series Volume 1
Ivona Cetinić, Charles R. McClain, and P. Jeremy Werdell, Editors
ACE Ocean Working Group recommendations and instrument requirements for an advanced ocean ecology mission
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NASA/TM–2018-219027/ Vol. 1
February 2018
PACE Technical Report Series Volume 1 Ivona Cetinić, Charles R. McClain, and P. Jeremy Werdell, Editors
ACE Ocean Working Group recommendations and instrument requirements for an advanced ocean ecology mission
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INTRODUCTION Introduction to this volume and series
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; https://pace.gsfc.nasa.gov) mission represents NASA’s next great investment in satellite ocean color and the combined study of Earth’s ocean-atmosphere system. At its core, PACE builds upon NASA’s multi-decadal legacies of the Coastal Zone Color Scanner (1978-1986), Sea-viewing Wide Field-of-view Sensor (SeaWiFS; 1997-2010), Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (1999-present) and Aqua (2002-present), and Visible Infrared Imaging Spectroradiometer (VIIRS) onboard Suomi NPP (2012-present) and JPSS-1 (2017-present; to be renamed NOAA-20). The ongoing, combined climate data record from these instruments changed the way we view our planet and – to this day – offers an unparalleled opportunity to expand our senses into space, compress time, and measure life itself.
This volume marks the first in new NASA Technical Report series that will document the scientific activities of and the supporting formulation decision criteria for the PACE mission. As demonstrated by the SeaWiFS Project, such a series ensures the availability of critical information on the mission, instruments, and algorithms to the scientific community. The PACE Project hopes this series will continue and build upon spirit of openness and collaboration within the community begun by the SeaWiFS Technical Report series over 25 years ago. The second volume will present the PACE Science Definition Team Report (originally released in 2012). At the time of this writing, we are actively preparing additional volumes, as well – one on scientific analyses and activities related to mission architecture, another on mission and instrument requirements, and another on spectrometer design – all of which were conducting during mission phases Pre-Phase A (2014-2016; pre-formulation: define a viable and affordable concept) and Phase A (2016-2017; concept and technology development). The mission entered Phase B (preliminary design and technology completion) in July 2017. We chose to use these first two volumes, however, to present material that predates the mission itself, offering insight into how PACE came to be.
While NASA Headquarters officially realized the PACE mission in January 2014, the idea of PACE extends back well over a decade. Formulation of PACE science objectives and instrument requirements commenced in 2000 with a NASA agency-wide carbon cycle initiative that spanned ocean, land, and atmosphere disciplines. Over time, the concept of an advanced ocean biology mission matured into what has become PACE, with much of the evolution occurring under the auspices of the Aerosol, Cloud, and ocean Ecosystems (ACE; 2008-present) mission. The ACE ocean working group, in particular, poured substantial effort into mission formulation and derivation of requirements for an advanced space-borne spectroradiometer that advanced the legacies of SeaWIFS and MODIS. In the end, the PACE Project views this ACE ocean white paper as the first documentation of the vision, science rationale and measurement requirements of what has become the PACE mission. With that in mind, we (the PACE Project) are deeply indebted to the ACE ocean working group and, accordingly, present their white paper as the first volume of the PACE Technical Report series.
P. J. Werdell PACE Project Scientist November 2017
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Introduction to this volume Our Earth is home to over seven billion people. The ocean covers 71% of the Earth’s surface, and global Earth observing satellites allow us to see a view of our Earth and its ocean about every two days. The beauty of our ocean planet is profound and its importance to humanity, our health, and economy is staggering. As of 2010, 80% of the world’s population lives within 60 miles of a coast. Fourteen percent of U.S. counties that are adjacent to the coast produce 45 percent of the nation's gross domestic product (GDP), with close to three million jobs (one in 50) directly dependent on the resources of the oceans and the Great Lakes. The Food and Agriculture Organization of the United Nations estimates that fisheries and aquaculture assure the livelihoods of 10-12 percent of the world’s population, with more than 90 percent of those employed by capture fisheries working in small-scale operations in developing countries. From jobs to food to recreation to regulating climate, the ocean is vital to all life on Earth.
The advent of satellite oceanography in the late 1970’s allowed Earth scientists to have a wide-angle lens with which to view the vast expanses of life near the ocean’s surface. Complementing ship-based snapshots of the ocean’s biology and chemistry, satellites provided an ability to have a global, long-term view of the ocean from space. The Earth’s aquatic realm is immense, and prior to satellite observations the oceans remained critically undersampled in time and space. Methods that improve the temporal and spatial coverage offset this problem and infallibly lead to major leaps in our understanding of the oceans. The partnership between in-water (e.g., ship, mooring, drifter) and satellite ocean sampling is essential and must, at a minimum, persist so we can understand and protect our home planet. Such measurements are particularly critical during the current era of varying and rapidly changing climate. Explosive growth of human populations along coastal margins places increasing pressure on dynamic coastal and aquatic ecosystems, modifying natural processes and, in many cases, putting life, health, and property at risk from hazards inherent to the ocean.
Ocean color remote sensing began with the launch of the Coastal Zone Color Scanner (CZCS) proof-of-concept mission in 1978. What have CZCS and other ocean color data revealed? To start, we have confirmed that a close coupling exists between ocean climate and primary production. We know that the biologically productive ocean is extremely sensitive to vertical mixing. We have also verified the general Sverdrup/Riley concepts: that the combination of vertical mixing and light in a water column has major effects on the seasonal and temporal appearance of phytoplankton in the ocean. Satellite data of the ocean also allow ready identification of ocean and coastal fronts, which are key sites of high productivity and support tremendous upper trophic level biomass. Global ocean satellite data have also improved our understanding of important interactive relationships between coastal (e.g. squirts, jets, eddies) and oceanic waters, revealing a far greater influence of coastal processes on global ocean basins than anticipated. A global ocean view has additionally enabled previously unattainable synoptic estimates of primary production that can be resolved seasonally and decadally.
In mid-September of 2007, the ocean research community achieved a major milestone as NASA and industry partners marked the collection of ten uninterrupted years of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color data. Discovery and confirmation of oceanographic phenomena from ocean color continued with SeaWiFS, and included the impact of sunlight absorption by phytoplankton on the heat budget of the ocean, and elucidation of the linkage between biological production, associated carbon fixation, and climate. These findings and others concerning light penetration, photosynthesis, and phytoplankton growth within the oceans confirmed ideas that were established long before satellites existed. However, the use of satellites grounded these theories concerning the ocean biosphere and placed these theories within the context of Earth’s global ecology.
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The ten-year time series of SeaWiFS data allows researchers to take the pulse of Earth’s living oceans on a global scale in near-real time. Ten years beyond that and with a range of international ocean color missions and sensors, we now find ourselves standing on the threshold of possible breakthroughs in ocean biological and chemical sciences. Critical to this progress have been advances in our understanding of ocean circulation and cryospheric science aided by additional satellite observations. These data streams support research needs as well as applications (internationally) with regard to defense, fisheries management, environmental and water quality, shipping, and recreation.
The decade of global, research-quality satellite ocean color data is a significant achievement and rightly celebrated. However, as a community devoted to the stewardship of Earth, we must emphasize the need for a sustained, long-term commitment to continuous, global, research quality colorimetric observations. Despite the profound advances already achieved in understanding our ocean gardens, much of the global ocean remains unexplored and its ecology a mystery. We have a responsibility to continue today’s minimum set of key climate observations of ocean biology and chemistry. There is also a clear path to move international ocean color remote sensing well beyond current capabilities and begin exploring new questions and enabling new discoveries regarding our ocean gardens.
The past three decades have given us only a brief glimpse of a constantly changing Earth system in which natural and human factors interplay. We know that ocean ecosystems and their biogeochemical cycles are extremely complex. It is therefore incumbent upon us to inspire and support the next generation of satellites and Earth scientists. Discoveries await in Earth’s living oceans. Our path may lead to insights on global change, some perhaps we have not yet considered. However, the time series of satellite ocean color data to provide the basis of future exploration and enable both these discoveries and climate research is not guaranteed.
To gain insight in to climate variability and change, one requirement is a continuous time series of observations to estimate ocean properties such as phytoplankton chlorophyll a with the radiometric accuracy of SeaWiFS or better. Describing and quantifying new properties of ocean biology and chemistry from satellites allows developments in basic research, such as the mechanistic understanding of phytoplankton physiology, habitat health, and carbon fluxes, to move from the laboratory to the global context of Earth’s biosphere. These advances require an evolution in satellite instruments and missions beyond traditional measurements that enable scientific discovery. And it is here that the challenge lies; to ensure that developments in ocean color remote sensing match the rapid pace of scientific research.
Without global ocean color satellite data, humanity loses its capacity to take Earth’s pulse and to explore its unseen world. It is our duty to provide a long-term surveillance system for the Earth, not only to understand and monitor the Earth’s changing climate, but to enable the next generations of students to make new discoveries in our ocean gardens as well as explore similar features on other planets.
This technical report series is the product of these efforts. It outlines a strategy for the international community to lead in the scientific application of remote sensing technologies for the exploration and understanding of biological and biogeochemical processes of our oceans. This effort is absolutely critical to ensure the sustainability of our ocean economy, protect our home planet, and inspire the next generation of explorers and scientists, mathematicians and writers. The vision is to fully discover the mechanisms and interactions that sustain life on our ocean planet, reaching for new heights technologically, and revealing the unknown for the benefit of humankind.
P. S. Bontempi PACE Program Scientist November 2017
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ACE Ocean White Paper TM preface
Formulation of science objectives and sensor requirements for an advanced ocean biology satellite mission began in 2000 with a NASA agency-wide carbon cycle initiative that included ocean, terrestrial, and atmospheric disciplines [Gervin et al., 2002; McClain et al., 2002]. Details on an advanced ocean biology mission matured over a series of subsequent studies:
- The Physiology-Multispectral Mission (PhyLM) was developed in 2004 at the NASA Goddard Space Flight Center (GSFC) in preparation for an expected NASA Pathfinder opportunity. PhyLM was a 2-instrument concept and included an aerosol lidar. We were the co-PIs. The solicitation was never released.
- The Ocean Carbon, Ecology and Near-Shore (OCEaNS) mission concept was presented at NASA Headquarters in 2005 and then submitted as a mission concept white paper to the National Research Council Earth Sciences Decadal Survey (NESDS) solicitation by ourselves and Jay Herman (NASA GSFC). OCEaNS was a 3-instrument concept and included an aerosol lidar and a polarimeter.
- The Global Ocean Carbon, Ecosystems, and Coastal Processes (GOCECP, 2006) mission was a study requested by NASA Headquarters in response to the NESDS. The GOCECP study was conducted at NASA GSFC with one key objective to estimate the cost of an advanced ocean color mission (ocean sensor only). We were the science leads for that study.
- The Earth’s Living Ocean, a report by the NASA Ocean Biology and Biogeochemistry Working Group [2007] on mission priorities for the ocean biology and biogeochemistry program organized and lead by Paula Bontempi, the NASA program manager. We were members of the working group.
- The Aerosol, Cloud, and ocean Ecosystems (ACE, 2008-present) mission was recommended by the NESDS. The ACE mission was initially a 4-instrument mission including an advanced ocean color sensor, aerosol lidar, and a polarimeter (consistent with the OCEaNS mission concept), plus a dual-frequency cloud radar. Hal Maring and Paula Bontempi were the Headquarters program scientists. We served as leads for the ocean science component of ACE. The ACE ocean white paper, published herein, was drafted in 2010 with the final version submitted to the ACE program scientists in 2011. Similar white papers were submitted by the aerosol, cloud, and ocean-atmosphere interaction working groups.
One outcome of the ACE mission studies was the Pre-ACE concept, where the two passive sensors (ocean radiometer and polarimeter) would be launched earlier than the active sensors (lidar and radar). The idea was that the passive sensors were more mature in their development and would likely have sufficiently long lifetimes that the active sensors (with anticipated shorter lifetimes) could be launched somewhat later to create the desired constellation of simultaneous observations from all four instruments.
In 2010, NASA released the document Responding to the Challenges of Climate and Environmental Change [NASA, 2010] which, among other recommendations, called for a “highly calibrated Ocean Ecosystem Spectroradiometer (OES)” co-manifested with a multi-spectral and multi-angle polarimeter. This Pre-ACE mission concept was renamed as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. The PACE mission was directed to GSFC in late 2014.
We suggested to Jeremy Werdell, PACE Project Scientist, that the ACE ocean white paper be published as a NASA Technical Memorandum by the PACE Project because the white paper is the first documentation of the science rationale, objectives, and measurement requirements of what has become the PACE mission. The ACE ocean working group devoted much time and thought into mission
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formulation. We believe these contributions are reflected in the ACE white paper and deserve publication in an official, citable document.
M. J. Behrenfeld and C. R. McClain
ACE Ocean Working Group Leads
November 2017
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Contents
Chapter 1 - ACE Ocean Ecosystems
1.1. Introduction ....................................................................................................................................... 1
1.2. Approach ......................................................................................................................................... 10
1.3. Measurement & Mission Requirements .......................................................................................... 11
1.4. Calibration, Validation, and Observations in the Field ................................................................... 13
Appendix to Chapter 1 - ACE Ocean Ecosystems Ocean Ecosystems Sensitivity Analyses and Measurement Requirements
2.1. Remotely Sensed Ocean Optical, Biological, and Biogeochemical Parameters ............................. 16
2.2. Simulations for the NIR and SWIR SNR Requirements for Atmospheric Corrections .................. 23
2.3. Bio-optical model sensitivity analysis ............................................................................................ 25
2.4. Measurement requirements summary ............................................................................................. 28
GLOSSARY OF TERMS ........................................................................................................................... 30
References ................................................................................................................................................... 32
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Chapter 1
ACE Ocean Ecosystems
ACE Ocean Working Group, November 2011
1.1. Introduction The Earth’s ecosystems are comprised of a myriad of physical, chemical, biological and
ecological processes that create a variety of adaptive and resilient communities of organisms on both the
land and in the sea. These ecosystems are an integral part of the planet’s biogeochemical cycles (e.g.,
carbon, nitrogen, phosphorous, silica, iron, etc.), which, in turn, are coupled to and influence the planet’s
climate through feedback processes, many of which are not clearly understood. With the advent of
satellite ocean color technology, the global distribution of marine biosphere properties such as chlorophyll
concentration, water clarity, primary production, particle concentrations, and others can be routinely
surveyed and monitored over time. The data have been used, along with other biological and
hydrographic data, to identify ocean biogeochemical provinces and to define the ecological geography of
the ocean [Longhurst, 1998]. These capabilities, along with improved measurements at sea and numerical
models of ocean circulation and ecosystem dynamics, are revolutionizing our understanding of the marine
biosphere and how it interacts with the rest of the Earth system. The ACE ocean science objectives
represent a major advance in ocean ecosystem and biogeochemical research and require a huge step
forward from traditional satellite ocean color measurement capabilities. In this chapter, the science
objectives and rationale are outlined as are the commensurate measurement requirements.
The Carbon Cycle
In outlining the ACE mission, the Decadal Survey highlighted the need for continued measurement of
marine primary production to refine estimates of the air-sea exchange of CO2 and its long-term CO2
sequestration in the deep ocean. Implicit in this requirement is the need to understand how marine
ecosystems are changing and the corresponding temporal changes in the distribution and composition of
phytoplankton and the processes that are regulating these. Figure 1.1 depicts the global carbon cycle with
current estimates of the terrestrial, oceanic,
and atmospheric reservoirs and fluxes. The
deep ocean is, by far, the largest reservoir of
carbon readily available to the “active”
component of the carbon cycle. Of course,
there are larger reservoirs in sedimentary
rock formations and other geological
deposits, but these are essentially unavailable
except via the extraction and use of fossil
fuels as shown in Figure 1.1. The net uptake
of carbon by both the terrestrial and oceanic
systems is relatively small representing the
difference between much larger fluxes.
Estimates of biological CO2 incorporation
through net primary production are similar (~50 GtC/yr) for global terrestrial and ocean systems, and are
approximately matched by concurrent respiratory CO2 production and export to the deep sea. The
Figure 1.1. Global Carbon Cycle (modified from IPCC [2007]).
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anthropogenic CO2 source is 6.4 GtC/yr, with roughly half of this annual flux sequestered by ocean and
terrestrial systems. Ocean uptake is mediated through exchange with the overlying atmosphere, so as
atmospheric CO2 concentrations rise, the ocean concentration adjusts accordingly; but ocean uptake is
tied to the ocean’s bicarbonate system. Ocean biology modulates the bicarbonate system primarily
through the uptake of CO2 by photosynthesis. The surface equilibrium is disrupted by exchanges of
carbon with the deep ocean. These exchanges [Figure 1.1] are driven by ocean circulation (water mass
subduction and convection) and the sinking particle fluxes (the so-called “biological pump”). As Figure
1.2 implies, the carbon pathways and transformations in the ocean are complex and depth dependent.
Satellite observations are critical for measuring bio-optical and chemical properties near the surface and
models are essential to understanding how ocean ecological processes ultimately modulate the air-sea
fluxes and the exchanges with the deep ocean leading to the long-term sequestration of fossil fuel CO2.
Achieving an accurate quantification of the distribution, composition, and vertical fluxes of particles is a
key objective of the ACE mission.
The historical approach for quantifying ocean
primary production was to develop algorithms
based on chlorophyll concentrations (Chl), such as
that introduced by Behrenfeld and Falkowski
[1997]. Their algorithm also incorporated sea
surface temperature (SST) and photosynthetically-
available radiation (PAR), both of which are
available with high accuracy from satellite
observations. A problem with deriving chlorophyll
concentrations using ocean color is that other in-
water constituents also absorb blue light,
particularly colored dissolved organic matter
(CDOM), and are ubiquitous in the surface ocean
[Siegel et al., 2005]. The chlorophyll-a absorption
peak is at 443 nm, but CDOM absorption continues
to increase at shorter wavelengths. The CZCS did
not have any bands in the near-UV which would
have allowed for the separation of these pigments.
SeaWiFS, MODIS, and other “second generation” sensors have incorporated a band at 412 nm that allows
the retrieval of CDOM [see Siegel et al., 2005]. The impact of this uncertainty is illustrated in Figure 1.3,
which compares primary production estimates using the standard NASA chlorophyll algorithm (which
does not account for spatio-temporal variability in CDOM) and chlorophyll derived from a reflectance
inversion algorithm [Maritorena et al., 2002] that resolves CDOM variability. The difference in global
annual production for these two chlorophyll estimates is roughly 16 GtC/yr, representing an uncertainty
in annual ocean productivity of ~30%! Constraining this uncertainty requires extension of
measurement bands into the near-ultraviolet (to reduce uncertainties in CDOM retrievals) and improved
spectral resolution in the visible band to improve quantification of phytoplankton pigment absorption.
Figure 1.2. Ocean carbon cycle processes (modified
from Honjo [1997]).
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In addition to the challenge of accurately retrieving surface phytoplankton pigment
concentrations, current net primary production (NPP) algorithms do not accurately account for
phytoplankton physiological variability. Specifically, the concentration of phytoplankton pigments is a
function of both the standing stock of phytoplankton (biomass) and their physiological state (which
impacts intracellular pigment levels). Without distinguishing these two sources of variability,
accurate estimates of NPP cannot be made and consequently, neither can accurate estimates of
change over time. For example, if a decrease in surface chlorophyll is observed with increasing surface
temperature (as found over the SeaWiFS record), the change could be associated with a decrease in NPP
from decreasing stocks or growth rates or it could be associated with improved upper ocean light
conditions (photoacclimation) that is may be paralleled by no change or even an increase in NPP. One
more recent approach for addressing these issues in remote sensing data was described by Behrenfeld et
al. [2005] and Westberry et al. [2008]. In their approach, coincident remote sensing retrievals of
particulate backscattering coefficients and pigment absorption were used to quantify phytoplankton
carbon (C) biomass and physiological state (through Chl:C ratios). While representing a significant
conceptual step forward, this new approach remains compromised by inadequacies in current remote
sensing and field measurement capacities. In particular, relationships between particle backscattering and
phytoplankton biomass are dependent on the composition and particle size distribution of plankton
ecosystems. Also, relationships between Chl:C and phytoplankton growth rates are sensitive to variations
in the relative concentration of auxiliary photosynthetically active pigments. Furthermore, direct field
measurements of phytoplankton C concentrations for product validation are extremely difficult and time
consuming and thus are rare in historical databases. Consequently our understanding of Chl:C and its
relationships with growth rate and photoacclimation are largely limited to results from laboratory
experimentation. To address these serious issues, significant expansion of the UV-VIS spectral range and
resolution of remote sensing measurements are required to adequately improve estimates of particle size
distributions and phytoplankton pigment absorption. To support these expanded remote sensing
requirements, a significant and parallel effort is needed to establish field data sets of appropriate system
properties (e.g., phytoplankton C, absorption:C ratios, growth rate, acclimation irradiance) for product
validation.
The aforementioned developments (consistent with the ACE mission design) will make major
contributions toward constraining ocean productivity assessments and the accurate interpretation of
observed change, but represent only a portion of the ocean carbon system. A more complete
Figure 1.3. Net primary production as computed using chlorophyll-a derived from two different
algorithms, the NASA empirical radiance ratio algorithm and a reflectance inversion algorithm (also
called spectral matching).
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understanding of carbon budgets requires estimates not only of carbon fixation rates but of standing
stocks of carbon as well, including particulate organic carbon (POC), particulate inorganic carbon (PIC),
dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC). To this end, advances have
already been made in developing algorithms for POC [Gardner et al., 2006; Stramski et al., 2008], PIC
[Balch et al., 2005; Gordon et al., 2001], and calcification rate [Balch et al., 2007] from remote sensing.
Applying one such algorithm to current remote sensing data, for example, Balch et al. [2005]estimated
the standing stocks of particulate inorganic carbon (PIC, primarily calcite) and particulate organic carbon
to be 19 Mtons and 665 Mtons, respectively. Balch et al. [2007] further estimated the annual mean
calcification rate to be 1.6 GtC/yr, which is small compared to the primary production rate, yet important
for understanding changes due to ocean acidification. However, the efficiency of export of POC to PIC to
the deep ocean must be factored in when considering the relative contribution to deep ocean carbon
sequestration. The global determination of DOC remains elusive as DOC concentrations are not simply
related to CDOM [Nelson et al., 2010; Siegel et al., 2002]. However regional algorithms for estimating
DOC for coastal regions influenced by terrestrial inputs have been successful [e.g. Del Castillo and
Miller, 2008; Mannino et al., 2008]. Given that DOC is the largest ocean organic carbon pool, tracking
the global surface concentration distribution would be a significant achievement. DIC has no optical
signature and its concentrations must be modeled by knowing the fluxes in and out of the DOC pool.
Estimates of air-sea CO2 flux require ocean pCO2 values and an algorithm for the gas transfer function.
Signorini and McClain [2009] examined the global fluxes using the latest pCO2 climatology [Takahashi
et al., 2009] with various combinations of wind products and gas transfer functions. The range of net flux
values was 0.9-1.3 GtC/yr into the ocean which is somewhat less that that depicted in Figure 1.1. The
expanded capabilities of the ACE ocean radiometer will result in refined estimates of surface carbon
pools (e.g., PIC, POC) and rates (e.g., NPP) which can be assimilated into global models to constrain the
model estimates of carbon cycling in the water column and improve estimates of surface CO2 fluxes and
carbon export to the deep ocean.
Marine Ecosystems
The world’s oceans represent a mosaic of unique biomes and biogeochemical provinces. Longhurst
[1998] identified 56 pelagic provinces based on an examination of the seasonal cycles of phytoplankton
production and zooplankton consumption. While species composition can be diverse often a specific
phytoplankton species or functional type dominates. There are different ways of delineating these, e.g.,
size class (picoplankton, nanoplankton, etc.) and functional groups (diatoms, coccolithophores,
Trichodesmium, cyanobacteria, etc.). For instance in the subpolar North Atlantic, production early in the
year is due primarily to diatoms, but later in the summer, coccolithophores become abundant, preferring
more stratified conditions. Thus, depending on the physical environment, availability of macro- and
micro-nutrients, illumination, and the concentration of grazers, phytoplankton populations vary in their
biomass, species composition, photosynthetic efficiency, etc. These variations regulate primary
production and, therefore, higher trophic levels within the ecosystem, and play an important role in the
cycling of macro- and micro-nutrient concentrations. Identifying these distributions and properties and
how they change on seasonal and interannual time scales is key to understanding how ecosystems
function and how they respond to changes in the physical environment, whether natural or human-
induced.
Until recently, research on optical identification of specific species has focused on
coccolithophores and Trichodesmium because of their rather unique spectral reflectance signatures.
Coccolithophores are made of calcite platelets and can be identified in satellite data because, at high
concentrations, the reflectance is uniformly elevated across the spectrum. Global coccolithophore
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distributions were first assessed using CZCS data [Brown and Yoder, 1994]. As discussed above, calcite
can now be estimated from satellites and serves as an indicator of coccolithophore populations. However,
it is not an accurate indicator of viable coccolithophore cell concentrations because much of the calcite is
in the form of detached platelets. Coccolithophores prefer stratified conditions and are susceptible to
acidification. Thus, tracking calcite spatial distributions and concentrations over time will be a focus of
future ecosystem research as it relates to climate change. While this work may not require additional
spectral coverage in the future, it does require accurate sensor calibration and stability monitoring.
Another phytoplankton genus with a distinctive spectral signature is Trichodesmium, a
cyanobacterium. Trichodesmium have gas-filled vacuoles or trichomes, elevated specific absorption
coefficients below 443 nm, and uniformly high particle backscatter coefficients in the visible spectrum.
Trichodesmium is nitrogen-fixing and can bloom in areas of low ambient nitrate. Westberry et al. [2005]
found that if the concentration of trichomes is sufficiently high (3200 L-1), detection by SeaWiFS is
possible (and a sensor with greater SNRs could potentially detect lower concentrations). Westberry and
Siegel [2006] mapped the global distribution of Trichodesium, which was consistent with global
geochemical inferences made by Deutsch et al. [2007], and estimated that the blooms fix 60 TgN/yr
which is a four- to six-fold increase over estimates of just 20 years ago [Schlesinger, 1997]. Based on the
specific absorption spectrum, satellite observations below 412 nm should help improve quantification of
Trichodesium concentrations.
Going beyond coccolithophores and Trichodesium requires the separation of functional groups
with different pigment compositions and, therefore, subtle differences in reflectance spectra. Given the
limited number of spectral bands that heritage sensors have, separation is a challenge and the uncertainties
in the distributions must be high and are difficult to verify because only crude climatologies of species
distributions are available. used in situ databases of reflectance, pigments, functional groups and
SeaWiFS reflectances to estimate global open ocean distributions of haptophytes, Prochlorococcus,
Synechococcus-like cyanobacteria (SLC), and diatoms. However, the number of phytoplankton species or
size classes that can be estimated is currently limited by the number of SeaWiFS spectral bands.
Enhancing the spectral resolution and spectral range of ocean color measurements can greatly
enhance retrieved information on plankton composition. The approach for using such information is
referred to as “spectral derivative analysis” and has been demonstrated at ‘ground level’ by multiple
investigators. For example, Lee et al. [2007a] used 400 hyperspectral (3 nm resolution) reflectance
spectra from coastal and open ocean waters to examine taxonomic signatures in the first- and second-
order derivatives. Their analysis indicated very pronounced peaks representing slight spectral inflections
due to varying pigment absorption and backscatter characteristics of the water samples. An alternative
approach (differential optical absorption spectroscopy) was used by Vountas et al. [2007] and Bracher et
al. [2009], and applied to hyperspectral Scanning Imaging Absorption SpectroMeter for Atmospheric
CHartographY (SCIAMACHY) imagery (0.2-1.5 nm resolution) to derive global distributions of
cyanobacteria and diatoms. These studies show that realistic distributions of functional groups can be
extracted from satellite data and underscore the requirement for the ACE ocean radiometer to provide
hyperspectral data from the UV to the NIR.
In addition to retrieving information on phytoplankton composition through analyses of spectral
absorption features, studies have also been conducted on remotely characterizing particle size
distributions of natural plankton assemblages. Retrieved particle size distributions provide insight on
relationships between scattering coefficients and total particulate organic carbon, as well as the relative
contribution of various phytoplankton size classes to bulk standing stocks. Most recently, Kostadinov et
al. [2009] extended the work by Loisel et al. [2006] on spectral particle backscatter coefficient to derive
6
global distributions of dominant phytoplankton size classes contributing to total biomass [Figure 1.4].
Here again, higher spectral range and resolution than heritage ocean color bands will significantly
improve retrieved properties.
Phytoplankton Physiology
Phytoplankton acclimate to environmental conditions
(e.g., nutrients, temperature, and light) on time scales
from seconds to seasons. These physiological
adjustments influence their absorption spectra, growth
rates, C:chl ratios and other characteristics.
Intracellular changes in chlorophyll concentration in
response to variations in mixed layer light levels alone
can span over one order of magnitude, and
significantly influence our ability to accurately
interpret the satellite chlorophyll record and its
relationship to predictions of NPP (see above).
Behrenfeld et al. [2008] demonstrated phytoplankton
physiological variability was quantified from remote
sensing ratios of absorption and scattering properties
and provides an illustration of the magnitude of this
effect. However, accurate estimation of physiological
variability requires increased spectral information.
In addition to light effects on phytoplankton
acclimation states, the degree of nutrient stress (mild,
severe) and the type of nutrient stress (e.g., Nitrate,
Phosphate, Iron (Fe)) contribute a physiological
signature to remotely derived pigment fields and will
certainly be influenced by changing climate forcings on upper ocean ecosystems. One nutrient stress of
particular interest is that of iron limitation. The role of iron as a major factor limiting global
phytoplankton concentrations and primary production [Martin and Fitzwater, 1988]has been studied
through a number of iron enrichment experiments and modeling studies of aeolian dust transport and
deposition (see the ocean-aerosol interaction chapter). Diagnostic indicators of iron stress have also been
developed for field deployments, including expression of the photosynthetic electron acceptor,
flavodoxin, which replaces ferridoxin under low iron conditions [LaRoche et al., 1996], and unique
fluorescence properties of the oxygen-evolving photosystem II complex associated with iron stress
[Behrenfeld and Boss, 2006]. From this field-based fluorescence study, Behrenfeld et al. (2006) predicted
that satellite fluorescence measurements may provide a means for assessing global distributions of iron
stress. In a subsequent study, Behrenfeld et al. [2009]used MODIS fluorescence line height (FLH) data to
calculate global fluorescence quantum yields (), corrected for effects of pigment packaging and non-
photochemical quenching, and demonstrated a strong correspondence between elevated values, low
aeolian dust deposition, and model [Moore and Braucher, 2008; Moore et al., 2006; Wiggert et al., 2006]
predictions of iron limited growth [Figure 1.5]. These studies further demonstrate the potential for
Figure 1.4. Global distributions of three particle size
classes (modified from Konstadinov et al. [2009]).
7
extracting basic information on ecosystem properties far beyond simply measuring chlorophyll-a and are
significant design drivers for ACE.
Near-shore and Estuarine Processes
Unlike the SeaWiFS and MODIS (sensors designed for
open ocean scientific objectives), the ACE science
objectives also involve optically complex ocean
margins, the land-sea interface, and larger estuarine
systems, and must be capable of supporting coastal
management and environmental monitoring
requirements as well. These areas form the interface
between the terrestrial and open ocean provinces and
are the sites of very high primary production rates and
biogeochemical transformations of carbon, nitrogen,
and phosphorous. These processes are particularly
important where freshwater discharge from major
terrestrial drainage basins and or/population centers are
focused, e.g., Mississippi River delta, Chesapeake Bay,
San Francisco Bay, Gulf of Maine, Pamlico Sound,
and Pudget Sound. There is considerable debate within
the science community as to what fraction of global marine primary production and carbon sequestration
actually occurs in these areas and adjacent continental shelves and the “continental shelf pump” [Muller‐Karger et al., 2005; Tsunogai et al., 1999; Yool and Fasham, 2001]. Muller‐Karger et al. [2005]
estimated that the continental margins account for more than 40% of the global ocean carbon
sequestration. However, satellite primary production algorithms do not work in many continental shelf
and shallow water regions like the U.S. southeast shelf [Signorini et al., 2005]. Deriving more accurate
estimates of primary production and related quantities in complex coastal regions is a primary ACE
science objective.
Other ACE requirements are associated with water quality and red tides. Eutrophication, a
depletion of water column oxygen due to the decay of high concentrations organic matter often resulting
from enhanced agricultural/sewage runoff nutrient loads, is a serious problem in many coastal regions.
The Mississippi River outflow hypoxic or “dead zone” is one highly publicized example [Goolsby, 2000].
This condition has a significant impact on ecosystem health, commercial fisheries, and even tourism. In
some cases like the Baltic Sea, the events are linked to surface blooms of nitrogen-fixing cyanobacteria
which can be detected from satellite [Kahru et al., 2007]. In other cases, like the U.S. Gulf coast, there is
no strong correlation between surface chlorophyll and hypoxia [Walker and Rabalais, 2006] in which
case a sensor more capable than the present day instruments may facilitate new detection approaches.
While not necessarily related to eutrophication, harmful algal blooms (HABs) also affect fisheries and are
a threat to public health. Kahru and Mitchell [1998] showed that spectral coverage between 340-400 nm
is necessary for detecting red tides and more recent studies have focused on utilizing the MERIS 710 nm
band [Gower et al., 2005 ]. SeaWiFS and MODIS do not have these wavelengths. Groups within NOAA
responsible for red tide monitoring are currently using MERIS data to refine detection algorithms [Wynne
et al., 2008]. The ACE ocean radiometer should provide capabilities needed to support the most
advanced HAB detection algorithms.
In these waters, pigment and particulate complexes are more diverse and concentrated making the
spectral reflectances more varied [e.g. IOCCG, 2000]. As a result, the spectra are amplified in the red
Figure 1.5. Chlorophyll fluorescence yield (top) as
an indicator of soluble iron deposition (bottom;
model). Modified from Behrenfeld et al. [2009].
8
portion of the spectrum and depressed in the UV and blue. Heritage sensors did not exploit applications
in the green and red portions of the spectrum. In fact, some MODIS ocean bands saturate in turbid
waters, so care must be taken in specifying saturation radiances. Bands in the near-infrared (NIR) were
designed for aerosol corrections. However, turbid waters have finite reflectances in the NIR that
compromise the aerosol correction resulting in over-corrections. Over the past decade, a number of
algorithms to remove or avoid this problem have been developed, e.g.,Siegel et al. [2000] using NIR
bands, and Wang and Shi [2005] using MODIS shortwave infrared (SWIR) bands. For the 2009
SeaWiFS reprocessing, the latest NIR-based aerosol correction scheme resulted in significant
improvement in derived product data quality in areas like the Chesapeake Bay [Bailey et al., 2010;
Werdell et al., 2010]. As Werdell et al. [2010] point out, the MODIS SWIR bands do not have adequate
signal-to-noise ratios (SNR) to do the turbid water aerosol correction well. For ACE, SWIR bands with
adequate SNRs will be required (see the appendix). Also, the aerosols in many coastal regions absorb
radiation, and current algorithms do not handle these corrections. ACE ocean radiometer observations in
the near-UV, polarization measurements from the ACE polarimeter, and aerosol height measurements
from the ACE lidar coupled with improved aerosol models will greatly improve ocean color retrievals in
the complex atmosphere situations.
Over the past few years, the NASA Ocean Biology and Biogeochemistry program (OBBP)
recognized the need for expanding its science objectives into near-shore and estuarine waters and has
supported this new emphasis through research solicitations, in situ instrumentation development,
incorporation of high spatial resolution data from MODIS (250 and 500 m bands) and MERIS (300 m)
into the SeaWiFS Data Analysis System (SeaDAS) software (NASA freeware developed and distributed
by the NASA Ocean Biology Processing Group at Goddard Space Flight Center), etc. This research and
development needs to continue to further lay the foundations for the ACE mission.
Physical-Biological Interactions
There are many ways in which physical processes influence biological processes. These include not only
ocean dynamical processes like coastal and equatorial upwelling, vertical mixing due to physical stirring
and buoyancy effects, ocean frontal circulation, horizontal advection, etc., but also meteorological
conditions such as cloud cover, surface winds, aeolian transport and deposition of dustborne nutrients
(iron), etc. These processes modulate the flux of nutrients into the euphotic zone and illumination, which
together regulate photosynthesis and other geochemical reactions. Most of these processes have been
incorporated into coupled circulation-biogeochemical models either on regional or global scales. There is
a huge published literature (theoretical, observational, and model-based) on these topics, particularly on
physical forcing of biogeochemistry. However, until recently, the feedbacks of biogeochemistry on ocean
dynamics have not been considered or were thought to be of secondary importance. Also, many of the
biogeochemical feedbacks are linked to aerosol and cloud properties and are discussed in the ocean-
aerosol interaction chapter of this document
Biogeochemical feedbacks on ocean circulation are principally effects associated with radiation
absorption near the surface that affects stratification, sea surface temperature, and air-sea heat exchange.
Some early studies using satellite ocean color data highlighted “penetration radiance” [Lewis et al., 1990;
Ohlmann et al., 1996] which is the radiance that penetrated through the mixed layer and is, therefore, not
available to the coupled ocean-atmosphere system. The penetration of visible light is mostly determined
by phytoplankton pigment concentrations, dissolved constituents, and particle concentrations. In low
concentration waters like the western Pacific warm pool where the mixed layer is shallow, this radiant
flux can be 10-15% of the incident solar irradiance [Ohlmann et al., 1996; Siegel et al., 1995]. In areas
where pigment concentrations are high, more sunlight is absorbed near the surface resulting in warmer
9
temperatures. A consequence of sunlight absorption by phytoplankton is a global amplification of the
seasonal cycle of SST [Frouin et al., 2000]. The related changes in surface layer temperature and
stratification modify surface circulation and SST patterns at levels substantial enough to impact
atmospheric circulation, particularly in the tropics [Murtugudde et al., 2002; Shell et al., 2003].
Science Questions and Objectives
To summarize, the science objectives of the ocean biogeochemistry community have expanded
remarkably over the past twenty years. This progress has been greatly facilitated by data sets from
SeaWiFS and MODIS, advances in marine optics (theoretical and experimental), and a growing concern
about the impacts of climate change on marine ecosystems. These new objectives require much more
robust measurement systems (see text below for details), especially in terms of spectral coverage. From
the discussion above, the following set of ocean science questions are being posed for the ACE mission.
• SQ-1: What are the standing stocks, composition, & productivity of ocean ecosystems? How and
why are they changing?
• SQ-2: How and why are ocean biogeochemical cycles changing? How do they influence the
Earth system?
• SQ-3: What are the material exchanges between land & ocean? How do they influence coastal
ecosystems, biogeochemistry & habitats? How are they changing?
• SQ-4: How do aerosols & clouds influence ocean ecosystems & biogeochemical cycles? How do
ocean biological & photochemical processes affect the atmosphere and Earth system? These
questions link directly to Question 4 of the Ocean-Aerosol Interactions element of the ACE
program.
• SQ-5: How do physical ocean processes affect ocean ecosystems & biogeochemistry? How do
ocean biological processes influence ocean physics?
• SQ-6: What is the distribution of algal blooms and their relation to harmful algal and
eutrophication events? How are these events changing?
These questions are directly related to the objectives of the NASA Ocean Biology and Biogeochemistry
Program as outlined in its long-term planning document, The Earth’s Living Ocean, The Unseen World
[NASA OBB Working Group, 2007].
The Human-Ocean Relationship and Societal Benefits
For Earth’s entire history, only one life form has ever existed with the capacity to intentionally modify the
role of ecosystems on the global environment: humans. We are the ultimate caretakers of this planet and
it is upon our shoulders that the well-being of the biosphere rests. Our recognition of this responsibility,
however, is recent – emphasized by escalating impacts of an ever-growing human population. In
addition, and despite a long history of technological advancement, we remain intimately dependent on the
biosphere’s highly interlaced food webs for our well being. Complex ocean ecosystems provide habitat
and natural resources that nurture biodiversity, interact with geochemical and physical systems in the
cycling of carbon and other elements, and play an essential role in the regulation of climate over annual to
geologic time scales that contributes importantly to the habitability of our planet. At the same time, ocean
ecosystems are fragile and highly susceptible to environmental change. One must only look at the mass
extinction events in Earth’s history to fully appreciate the delicate balance of species diversity and
10
ecosystem resilience and to understand its dependence on stability in climate and biological conditions.
Today, threats to ocean ecosystems come not only from natural sources, but from human activities as
well, with the human component becoming ever more prominent and well documented. As caretakers of
this unique living planet, we are charged with the responsibility of understanding causes and effects of
global change and protecting the diversity and invaluable services that the global oceans provide.
Understanding functional relationships within the living ocean, along with ocean-land-
atmosphere feedbacks, represents a major challenge to the science community and one to which the ACE
mission is particularly well poised to contribute greatly. Through its advanced observation sensor suite,
this mission will allow better description and prediction of Earth system mechanisms affected by natural
and anthropogenic climate changes, and assessment of how these processes feedback on the overall Earth
system over time. At the same time, the ACE mission will provide a more diverse set of capabilities and
data products of higher quality in coastal regions than current sensors, e.g., harmful algal bloom detection,
suspended sediment concentrations, and carbon pools. The improved understanding and measurement
capabilities will enable informed national policy, improved resource management practices, and
decreased threats to our economy, health, safety, and national security.
1.2. Approach Addressing the key outstanding science questions for ocean ecosystems requires significant advances in
remote sensing capabilities beyond heritage sensors, improvements in strategies to remove contamination
of ocean color signals by the atmosphere, and well-developed field- and on-orbit calibration and
validation approaches. With the suite of ACE sensors and their advanced capabilities, more accurate- and
a broader set of key ecosystem properties can be characterized globally on weekly to shorter time scales.
Some of these properties are essential for answering all of the science questions outlined above, while
others are targeted toward advancing understanding of a particular science issue. In some cases, the
observational data largely functions to inform an overarching model, but in all cases the required set of
retrieved properties creates the link between the Science Questions and Measurement and Mission
Requirements.
SQ-1 (Ocean Ecosystems) Approach: Quantify phytoplankton biomass, pigments, and optical properties,
assess key phytoplankton groups (e.g., calcifiers, nitrogen fixers, carbon export), and estimate particle
size distribution and productivity using bio-optical modeling, chlorophyll fluorescence, and ancillary data
on ocean physical properties (e.g., SST, MLD, etc.). Validate these retrievals from pelagic to nearshore
environments.
SQ-2 (Ocean Biogeochemical Cycles) Approach: Retrieve phytoplankton biomass and functional
groups, POC, PIC, DOC, PSD and productivity from remotely sensed ocean properties. Validate these
retrievals from pelagic to nearshore environments. Assimilate ACE observations in ocean
biogeochemical models to provide fields for missing observations (cf., air-sea CO2 fluxes, export, pH,
etc.).
SQ-3 (Land-Ocean Interactions) Approach: Quantify particle abundance, dissolved material
concentrations and their physical and optical properties. Validate these retrievals from coastal to
estuarine environments. Compare ACE observables with ground-based and model-based land-ocean
exchange in the coastal zone, physical properties (e.g., winds, SST, SSH, etc), and circulation (ML
dynamics, horizontal divergence, etc).
SQ-4 (Atmosphere-Ocean Interactions) Approach: Quantify ocean photobiochemical and
photobiological processes and atmospheric aerosol loads and distributions. Combine ACE ocean and
11
atmosphere observations with models and other remotely retrieved fields (e.g. temperature and wind
speed) to evaluate (1) air-sea exchange of particulates, dissolved materials, and gases and (2) impacts on
aerosol and cloud properties. Conduct field sea-truth measurements and modeling to validate retrievals
from the pelagic to near-shore environments.
SQ-5 (Bio-physical Interactions) Approach): Compare ACE ocean observations with measurements of
physical ocean properties (winds, SST, SSH, OOI assets, etc.) and model-derived physical fields (ML
dynamics, horizontal divergence, etc.). Estimate ocean radiant heating and assess feedbacks. Validate
from pelagic to nearshore environments.
SQ-6 (Algal Blooms and Consequences) Approach): Measure key phytoplankton biomass, pigments and
key group abundance including harmful algae. Quantify bloom magnitudes, durations, and distributions,
assess inter-seasonal and inter-annual variations, and compare variability to changing
environmental/physical properties. Validate these retrievals from pelagic to nearshore environments.
1.3. Measurement & Mission Requirements SeaWiFS and MODIS ocean requirements were defined in the 1980s with an emphasis on global open
ocean observations of chlorophyll-a. Both sensors addressed major deficiencies in the proof-of-concept
CZCS design and calibration/validation programs, e.g., the addition of NIR bands for atmospheric
correction and mission-long on-orbit and field calibration measurements. Since then, the ocean optics and
marine biology communities have developed capabilities and applications that far exceed the spectral
coverage of these sensors and experience using these sensors has provided many “lessons learned”. Also,
development of climate data records requires consistent sets of spectral bands and stringent mission-long
stability specifications. Together, this cumulative experience has highlighted a number of new
requirements, including the following enhancements which are incorporated into the ACE ocean
measurement requirements. A table is provided in the appendix below that lists 26 discrete bands and
specific applications of each. Note that the table includes the SeaWiFS and MODIS bands for continuity.
1. Major advances in global ocean biological studies have been realized through the development of
spectral inversion algorithms. These algorithms allow the simultaneous and mutually consistent
retrieval of multiple in-water properties, including phytoplankton absorption, absorption by
colored dissolved organic material, and particulate backscattering coefficients. SeaWiFS and
MODIS do not provide adequate spectral coverage for optimizing the inversions. Requirement:
19 bands at specific center wavelengths between 360-748 nm characteristic of key constituent
absorption and scattering features. (Related Science – SQ-1, SQ-2, SQ-4, SQ-5, SQ-6)
2. The addition of a 412 nm band into SeaWiFS and MODIS has provided a first look at the
separation of chlorophyll-a and CDOM using satellite ocean color imagery. Within the UV,
CDOM dominates light absorption for nearly all natural waters and improvements in separation
between chlorophyll-a and CDOM will occur from including measurements of water-leaving
radiance in the UV. Requirement: additional bands at 360 and 380 nm. (Related Science – SQ-1,
SQ-2, SQ-3, SQ-4)
3. Uncertainties in current inversion retrievals by passive sensors can be reduced through
simultaneous and independent measurements of particle scattering using active sensors. Recent
analyses of CALIPSO data have demonstrated that space-based lidar systems can provide active
measurements of subsurface scattering and possibly information on vertical distributions of
particles. Requirement: Lidar measurements with parallel and perpendicular retrievals at an
12
ocean-penetrating wavelength and with a vertical resolution of 2 meters subsurface. (Related
Science – SQ-1, SQ-2, SQ-3, SQ-6)
4. Recent research has shown that phytoplankton fluorescence quantum efficiency can be derived
from MODIS fluorescence data and that the quantum efficiency is related to iron limitation or
stress. Requirement: Fluorescence line height bands consistent with the MODIS fluorescence
line height bands for time series continuity. (Related Science – SQ-1, SQ-2, SQ-5, SQ-6)
5. The research community has also explored the identification of phytoplankton functional groups
using SeaWiFS and MODIS, but with high uncertainties. Recent analyses using hyperspectral
SCIAMACHY on Envisat have demonstrated the promise of spectral derivative analyses.
Requirement: 5 nm resolution data from 360 to 755 nm. (Related Science – SQ-1, SQ-2, SQ-6)
6. Studies in optically-complex coastal waters have identified limitations in the use of blue and
green bands for quantifying chlorophyll. Requirement: Additional bands in the red, and near-
infrared, including 700-750 nm range, are necessary. (Related Science – SQ-3, SQ-6)
7. Accurate satellite retrievals of water leaving radiances require robust and accurate corrections for
atmospheric contributions to top-of-atmosphere (TOA) radiances. In short, the ocean color
problem is one of low ‘signal to noise’, as greater than 90% of TOA radiances can be from the
overlying atmosphere. In some circumstances (e.g., presence of Asian or African dust) current
atmospheric corrections suffer from inadequate information on these aerosol optical properties
and their vertical distribution. Requirement: Lidar ‘curtain’ measurement of aerosol distributions
with 0.5 km vertical resolution and polarimeter broad spatial coverage to retrieve aerosol heights
and single scatter albedo. Include UV bands (Item 2 above) to assist in the detection and
atmospheric correction of absorbing aerosols. (Related Science – All SQ’s)
8. Certain required bio-optical bands overlay water vapor absorption features making corrections
necessary. Requirement: Include an 820 nm band to quantify water vapor concentration.
(Related Science – SQ-1, SQ-4,SQ-6)
9. Programmatic research objectives oriented towards turbid coastal waters must address finite
reflectances in the NIR bands which compromise the aerosol correction. Requirement: SWIR
bands at 1245, 1640, and 2135 nm with substantially higher SNRs than the equivalent MODIS
bands. (Related Science – SQ-3, SQ-6)
10. The direct lunar calibration (Earth-viewing optics only) and in situ vicarious calibration of
SeaWiFS, in particular, successfully demonstrated the climate quality ocean biology data sets
necessitate highly accurate independent temporal stability monitoring (0.1% stability knowledge
over the duration of the mission) and gain adjustments, respectively. The vicarious calibration
required a multi-year time optical mooring deployment to establish stable gain factors.
Requirement: Monthly lunar views at a fixed 7 lunar phase angle and at least one long-term
vicarious calibration time series. (Related Science – All SQ’s)
11. Concurrency of global data products is required to address many of the ACE Ocean Ecosystems
science questions and for ocean color data processing. These observations include SST, SSH,
vector winds, MLD, precipitation, and O3 and NO2 concentrations. Requirement: Concurrency of
operational satellite data and model output products. (Related Science – All SQ’s)
13
Other sensor requirements address sun-glint avoidance (sensor tilting), polarization sensitivity, SNRs,
image quality (straylight, stripping, crosstalk), 2-day global coverage frequency, data quantization, and
saturation radiances. To achieve the radiometric accuracy requirements of the inversion algorithms, well-
developed and tested technologies and methodologies for prelaunch sensor characterization must be
established in advance of flight unit testing. When all the requirements for passive ocean radiometry are
tallied, the comparison with heritage sensors is striking, particularly with respect to spectral coverage
[Figure 1.6].
Figure 1.6. Comparison of spectral coverage of heritage sensors and the OES.
1.4. Calibration, Validation, and Observations in the Field Observations made in situ are an essential and integral part of the ACE mission. Field work will consist of
observations useful for calibration and validation (e.g. measurements of optical properties), but will also
include measurements essential to answering the science questions that are not possible from the ACE
spacecraft-based instruments. An example of these sorts of observations would be measurements of
export flux (SQ-2) which occurs below the surface layer of the ocean and is thus not directly observable
by remote sensing. Field observations will also be critical in the prelaunch phase for the purpose of
developing algorithms or identifying proxy measurements that will employ the new capabilities of the
ACE spaceborne instruments. Previous experience with SeaWiFS, MODIS, and related ocean color
radiometers indicates that continuous assessment of calibration and validation of products, combined with
periodic reprocessing of complete mission data sets, is necessary throughout the lifetime of the mission.
Sustained mission-lifetime measurements will take place at selected time series sites for purposes of
vicarious calibration (mission requirement 8) and product validation, and these will be augmented by
measurements made on cruises and moorings of opportunity, and in intensive field campaigns. Product
14
validation and algorithm development will be facilitated by the continuation of the NASA SeaBASS
database, which stores bio-optical (and now biogeochemical) data collected concurrently with satellite
overpasses.
New sensor capabilities will require
corresponding capabilities in the field. A road
map for technology development is being
developed by the community that describes
necessary advancements required to make best
use of the future ACE sensor suite in solving
the science questions laid out by the ACE
Ecosystems working group. Figure 1.7 shows
the current state of the art for a list of
parameters that are required for particular
SQs, in terms of analytical capability and
deployment technology. For the ACE mission
this includes biogeochemical parameters in
addition to the traditionally measured
radiometric quantities. Pressing needs here
include extension of currently measured
radiometric and inherent optical properties
into the UV and further into the NIR.
Intensive field campaigns will be
designed to address particular interdisciplinary
questions with shipborne measurements
complementing the ACE sensor suite. Specific
proposed cruises/topics are being discussed by
the community now. Campaigns will be
designed to take maximum advantage of
suborbital resources (e.g. airborne and
shipborne measurements) to make best use of
the available remote sensing data. Topics will
include ocean-aerosol interactions in coastal
and open-ocean areas where atmospheric correction due to dust and other terrestrial aerosols has been
challenging for current sensor technology.
One strategy for efficiently pursuing ACE goals in interdisciplinary field campaigns is to
cooperate with national and international coordinating groups who are proposing large field programs.
Examples of these include OCB (Ocean Carbon and Biogeochemistry, http://www.us-ocb.org/),
addressing SQ-1 & SQ-2; SOLAS (Surface Ocean-Lower Atmosphere Study, http://www.us-solas.org/),
addressing SQ-4; and CLIVAR/Carbon Repeat Hydrography Project (http://ushydro.ucsd.edu/),
addressing SQ-2 & SQ-5.
Field Analytical Levels
Parameter CP RM UB PM TR M T G R A S
Radiometry C 1 2 3 4 5 6
Oceanic IOPs ? 1 2 3 4
Atm. Optical Properties 2 4 5
CDOM C 2 3 4 5
DOC C 2 3 4
POC C 1 2 3 4
PIC C 1 2 3 4
Vertical Flux ? 1 2 4 6
TSM 2 3 4
PON 1 2 3 4
Ammonium ? ? 2 3 4 6
Nitrate/Nitrite ? ? ? 1 2 3 4
Biological PP C 1 2 4 5 6
HPLC pigments C 1 2 4 6
Natural fluorescence C 1 2 6
MAAs 1 6
Micro Taxonomy ? 1 2 4 6
Pico Taxonomy ? 1 2 4 6
O2 ? 2 5 6
Salinity ? C 3 4 5 6
Temperature C 1 3 4 5 6
Surface meteorology ? 1 4 5 6
Particle size/abundance ? ? 1 3 4
DMS, DMSp 1 2 4
Silicate ? ? 1 2 3 4
Phosphate ? ? 1 2 3 4 6
pCO2 ? 4
Trace nutrients ? 1 2 4 5
pH ? ? 2 4Moor.
Tower
Glider
R/V
A/CSat.
CP: Community protocols (experimental method)
RM: Reference materials (research)UB: Uncertainty budget (semi-quantitative)PM: Performance metrics (quantitative)TR: NIST (or other) traceability (CDR)
Nitrogen
Cycle
Some capability demonstrated, but more work needs to be done.
Deployment Tech.Ocean Ecosystems
Science Questions
Carbon
Cycle
Optical
Science
Discipline
Mature capability (calibration and validation quality).
Readiness Capabilities for Analytical
Levels and Deployment Technologies
Physical
Chemical
Little capability demonstrated, significant work to be done.
Most stringent requirements are for validation of satellite climate data records (CDR, C)
Key to Analytical Levels
Figure 1.7. Ocean in situ/laboratory measurements requirements. The “?” implies the status is not known.
15
The ACE Ocean Working Group includes those who participated in ocean related activities such as
defining the ocean science objectives and questions, performed sensitivity analyses, provided ocean
parameter measurement specifications, participated in routine teleconferences, attended ACE meetings,
assisted in sensor and mission concept studies, and other deliberations reflected in this document and
appendices. This group included Z. Ahmad, B. Balch, M. Behrenfeld (co-chair), E. Boss, J. Chowdhary,
C. Del Castillo, R. Frouin, S. Gasso, H. Gordon, S. Hooker, Y. Hu, T. Kostadinov, Z. Lee, A. Mannino,
S. Maritorena, C. McClain (co-chair), N. Meskhidze, M. Mulholland, N. Nelson, D. Siegel, D. Stramski,
R. Stumpf, M. Wang, J. Werdell, and T. Westberry. Paula Bontempi has served as the NASA
Headquarters ACE ocean program scientist since the inception of the ACE mission definition activity in
2008.
16
Appendix to Chapter 1
ACE Ocean Ecosystems Ocean Ecosystems Sensitivity Analyses and Measurement
Requirements
ACE Ocean Working Group
The appendix has four sections on remotely sensed optical, biological, and biogeochemical parameters
related to the science questions, atmospheric correction sensitivity to noise, bio-optical algorithm
sensitivity to noise, and a summary of ocean radiometer measurement requirements. The atmospheric
correction analysis was conducted by Menghua Wang (NOAA/NESDIS) and Howard Gordon (U. of
Miami). The bio-optical algorithm study was conducted by Stephane Maritorena (UC/Santa Barbara)
using inputs from the Wang and Gordon study.
2.1. Remotely Sensed Ocean Optical, Biological, and
Biogeochemical Parameters Associated with each of the science questions is a set of geophysical parameters that must be measured in
order to address the question. Remote sensing retrievals for each of these parameters requires an
algorithm that transforms the basic water-leaving radiances or remote sensing reflectances into an
estimated value of that parameter over a range of values. In Table 2.1 on the following 6 pages, the
various desired geophysical parameters are shown in the left-most column. For each parameter, the
second and third columns indicate baseline and threshold ranges for the parameter, respectively. Here,
the ‘baseline’ range represents the full desired retrieval range for a given parameter and is the range of
values between the 1% and 99% region for the parameter frequency distribution. The ‘threshold’ range is
the required retrieval range for a given parameter and represents the 5% to 95% region of the frequency
distribution. Baseline and threshold values for the geophysical parameters defined in Table 2.1 were
based on analyses of both field and historical satellite measurements. Specific information regarding these
analyses for each parameter is provided in the right-most column of comments. It should be noted that
values for each parameter have been measured that exceed even the baseline range, but these values are
found under extreme and rare conditions and were not viewed as critical retrieval requirements for a
satellite mission focused on global ocean properties. Importantly, the range of parameter values shown in
Table 2.1 places specific requirements on ACE satellite radiometry regarding spectral bands, spectral
ranges, and data quality from the sensor (e.g., signal-to-noise ratio and radiometric accuracy), as well as
requiring algorithm development and algorithm sensitivity analyses.
Table 2.1. ACE ocean geophysical parameters, retrieval baseline ranges, and retrieval threshold ranges. See comments in right-most column regarding data sets and other notes on parameter ranges.
17
18
19
20
21
22
23
2.2. Simulations for the NIR and SWIR SNR Requirements for
Atmospheric Corrections Atmospheric correction for ocean color product is extremely sensitive to sensor spectral band
calibration errors, as well as to radiometric noise. This is due to the considerably low radiance from the
ocean compared to the sensor-measured top-of-atmosphere (TOA) radiance. The sensor spectral band
radiometric performance can be characterized by the signal to noise ratio (SNR). To understand the
radiometric noise effects on the derived normalized water-leaving reflectance spectra, simulations of
atmospheric correction, using the two near-infrared (NIR) bands (765 and 865 nm) and various
combinations of the shortwave infrared (SWIR) bands (1240, 1640, and 2130 nm), have been carried out
for several levels of sensor noise.
Noise Model. A Gaussian distribution (with mean value = 0) is used for the noise simulations. The
standard deviation (STD) of the Gaussian distribution is the radiance noise level (i.e., related to the SNR
values). The simulated reflectance noise is then added into the TOA reflectance at various NIR and
SWIR bands that are used for making atmospheric correction. Eight noise levels are generated,
corresponding to eight SNR values of 25, 50, 100, 200, 400, 600, 800, and 1000. It is noted that the
reflectance noise is only added into the bands that are used for atmospheric correction (e.g., two NIR
bands), and UV and visible bands are noise free in all simulations discussed in this subsection. The
reflectance noises are spectrally incoherent.
Atmospheric Correction. Atmospheric correction simulations using two NIR bands (Gordon and
Wang, 1994) and various SWIR bands [Wang, 2007] have been carried out including reflectance noise
levels for the corresponding NIR and SWIR bands. Specifically, simulations were carried out for a typical
Maritime aerosol model (M80) and a Tropospheric model (T80), where the T80 model is actually M80
model without the large size fraction, for aerosol optical thicknesses (at 865 nm) of 0.05, 0.1, 0.2, and 0.3.
The M80 and T80 aerosol models were not used in the aerosol lookup tables for atmospheric correction
[Gordon and Wang, 1994; Wang, 2007]. Simulations were performed for a case with solar-zenith angle of
60°, sensor-zenith angle of 45°, and relative azimuth angle of 90°.
SNR Simulations. For each case, atmospheric correction for 5000 noise realizations with a given
SNR value was carried out. For example, for a case with aerosol optical thickness (AOT 865 nm of 0.1,
5000 reflectance noise samples (with a given SNR value) were generated and added into the TOA NIR
(765 and 865 nm) reflectance values. The NIR atmospheric correction [Gordon and Wang, 1994] was
then performed 5000 times to generate the corresponding normalized water-leaving reflectance spectra
error. The same procedure was carried out for all four AOTs and also for the SWIR algorithm [Wang,
2007]. In the SWIR atmospheric correction, however, the Gaussian noise was of course added into the
SWIR bands (error free for UV to NIR bands). This produces the uncertainty in the derived normalized
water-leaving reflectance from the UV to the red (or NIR in the case of the SWIR bands). In effect, the
simulated uncertainty includes errors from both the atmospheric correction algorithm and the added
Gaussian noise in the NIR or SWIR bands. The reflectance uncertainty spectra (from UV to red) are then
used for the bio-optical model sensitivity analysis by Stephane Maritorena.
Example Results. Figure 1 provides sample results in the reflectance uncertainty spectra (UV to red
or UV to NIR) with simulations from atmospheric correction algorithm using the NIR or SWIR bands.
The error in the normalized water-leaving reflectance, [w()]N, is actually the standard deviation of the
derived uncertainty in [w()]N over the 5000 Gaussian noise realizations, i.e., each point in the plot was
derived from 5000 simulations ([w()]N errors were first obtained with these 5000 simulations and then
STD error was derived). The STD error was computed assuming that the mean value = 0 (i.e., error free).
24
Figures 2.1(a) and 2.1(b) are results for the NIR atmospheric correction algorithm (using 765 and 865 nm)
with the M80 and T80 aerosol models, respectively, while Figures 2.1(c) and 2.1(d) are results for the
M80 and T80 aerosols using the SWIR atmospheric correction algorithm (with bands of 1240 and 1640
nm) for various SNR values. Note that for the SWIR results [Figures 2.1(c) and 2.1(d)], errors in [w()]N
for two NIR bands are also included. Results in Figure 2.1 show that, as SNR value increases (or noise
decreases), error in [w()]N decreases (as expected), and it reaches the inherent algorithm error [Gordon
and Wang, 1994; Wang, 2007].
Figure 2.1. Error in the derived normalized water-leaving reflectance (in standard deviation with the mean value of 0) from 5000 Gaussian noise realizations as a function of the SNR value using the NIR (plots a and b) and SWIR (plots c and d) atmospheric correction algorithms. Aerosol model and AOT value, as well as solar-sensor geometry are indicated in each plot. For the NIR algorithm, error spectra data from UV to red are provided (plots a and b), while for the SWIR algorithm error spectra from UV to NIR are shown (plots c and d).
25
Figure 2.2 provides sample results in the reflectance uncertainty spectra as a function of the
wavelength (UV to NIR) for various SNR values with simulations from atmospheric correction algorithm
using the two SWIR band sets, i.e., 1240 and 1640 nm (Figure 2.2(a)) and 1240 and 2130 nm (Figure
2.2(b)). Importantly, results in Figure 2.2 show that errors in [w()]N from atmospheric correction are
spectrally coherent. In addition, Figure 2.2 demonstrates that a SNR value between ~200-300 for the
SWIR bands 1240 and 1640 nm is adequate (Figure 2.2(a)), while for the SWIR band 2130 nm a
minimum of SNR value ~100 is required. At these SNR values for the SWIR bands, the derived water-
leaving reflectance spectra from the SWIR atmospheric correction algorithms almost reach their
corresponding algorithm inherent accuracy. It should be noted that, however, with even higher SNR
values the derived [w()]N at the red and NIR bands can be further improved.
Summary. Atmospheric correction and bio-optical simulations (see results from Stephane
Maritorena) suggest that (1) for the NIR bands a minimum SNR value of ~600 is required, and (2) for the
SWIR bands at 1240 and 1640 nm a minimum SNR value of ~200-300 is required, while for the 2130 nm
band a minimum SNR value of ~100 is adequate.
2.3. Bio-optical model sensitivity analysis Simulations were performed to assess how noise in the spectral marine remote sensing
reflectance, Rrs(), affects the retrievals of biogeochemical variables from a semi-analytical ocean color
model (GSM01, Maritorena et al. [2002]). These analyses were performed in order to assess the required
SNRs in the ACE visible bands to ensure accurate bio-optical retrievals. Noise is created from the at-sea-
level atmosphere reflectance spectra derived from the atmosphere specific simulations ran by Menghua
Wang. The spectral atmospheric noise is added to a marine reflectance spectrum at the surface derived
from a chlorophyll-based model [Morel and Maritorena, 2001]. We compared the model retrievals
obtained when spectral reflectance is contaminated by noise to those retrieved from noise-free spectra.
These simulations were run for a variety of atmospheric and marine conditions. This is briefly described
below.
Two main kinds of noise were considered: 1) Atmospheric noise caused by errors in the NIR
bands and propagated to the visible bands and, 2) noise as a random, spectrally uncoherent fraction of the
Top-of-Atmosphere reflectance in addition to the NIR created noise. This latter case was designed to
represent radiometric noise from other sources than the NIR bands (e.g. calibration). These two cases,
will be referred to as "NIR" and "radiometric" errors, respectively.
Figure 2.2. Error in the derived normalized water-leaving reflectance (in standard deviation with the mean
value of 0) from 5000 Gaussian noise realizations as a function of the wavelength for various SNR values
using the SWIR atmospheric correction algorithm with two SWIR bands of (a) 1240 and 1640 nm, and (b)
1240 and 2130 nm. Aerosol model and AOT value, as well as solar-sensor geometry are indicated in each
plot.
26
In all runs, the "pure" marine Rrs signal ( = no noise) is generated from the MM01 model [Morel
and Maritorena, 2001] for 10 chlorophyll concentration (Chl) values in the 0.02-5 mg/m3 range (400-700
nm every 5 nm). The GSM01 retrievals from the inversion of these "no noise" spectra are the reference to
which the "noisy" NIR and radiometric cases are compared to.
For the "NIR" errors case, the at-sea-level reflectance spectra caused by errors in the NIR bands
(from Menghua Wang) are converted to Rrs, Rrs_NIR(), and added to a MM01 marine spectrum,
Rrs_MM01(, Chl), so
Rrs(, ocean) = Rrs_MM01(, Chl)+ Rrs_NIR() [Eq 2.1]
The resulting spectrum, Rrs(, ocean), is then inverted in GSM. The three GSM retrievals (Chl, CDM,
bbp) are then compared to the "no noise" case for 5000 spectra for each combination of SNR (8 values),
AOT(865) and atmospheric model (2 models) and marine Rrs() (10 spectra). The comparisons are
expressed in terms of the %rms for each of the GSM01 product and at each Chl level used to generate the
marine Rrs. The %rms is defined as rms*100/reference (reference = retrieval in the no noise case).
For the "radiometric" errors case, a random, Gaussian, spectrally uncoherent fraction of a TOA
signal is added to the marine spectra created similarly to what is described in the "NIR" case above. First,
TOA signals are constructed for a black ocean with the M80 and T80 models, AOT(865) = 0.1 and for
solar, sensor, and relative azimuth angles of 60, 45, and 90 degrees, respectively. The ocean contribution
to the TOA signal is calculated as a MM01 reflectance spectrum transmitted through the atmosphere
(with transmittance values matching the atmospheric model and geometry and AOT(865) of 0.05, 0.1,
0.2, and 0.3) and is added to the atmospheric TOA component (converted to Rrs units; Rrs_TOA()). The
fraction of the TOA signal that is added to the marine spectrum created as in the NIR cases is determined
through the generation of random Gaussian numbers with a mean of 0 and a standard-deviation of
1/SNR(visible) with SNR(visible) set to 10., 20., 40., 100., 200., 400., 800., 1000. and 2000. Then, each
wavelength of the TOA spectrum is multiplied by a unique random number (rn) and that fraction of the
TOA spectrum is added to the other components of the marine signal. This is done independently for each
of the 5000 spectra corresponding to each SNR(NIR)/AOT(865)/atmospheric model combination used in
the atmosphere simulations. In summary, in the "radiometric" errors case the at-sea-level Rrs is generated
as:
Rrs(, ocean) = Rrs_MM01(, Chl)+ Rrs_NIR() + (Rrs_TOA() * rn(, SNR(visible)) [Eq 2.2]
By looking at how much the retrievals from the noisy reflectance spectra depart from those derived
without addition of noise, it is possible to assess the SNR(visible) value that allows an acceptable
accuracy in the retrievals. It should be mentioned that in this approach, we assume an identical SNR level
throughout the visible spectrum and does not take into account the fluorescence bands. Figures 2.3 and
2.4 illustrate the results of these analyses.
27
Figure 2.4. Example of the %rms error for each of the GSM01 retrievals (green: CHL, red: CDM, black: BBP) as a
function of the CHL values used as input in MM01 for SNR(NIR)= 600 and different AOT(865) values. For each
retrieval, the curves for SNR(visible) of 200, 400, 800 and 1000 are plotted, the highest (=1000) and lowest (=200)
SNR(visible) values are indicated at either the beginning or the end of each curve.
Figure 2.3. Example of the average (solid lines and symbols) and standard-deviation (dotted lines) of the %rms error over the full range of Chl values used as input in MM01 for the 3 GSM01 retrievals (green: Chl, red: CDM, black: bbp) as a function of the SNR values in the visible and for SNR(NIR)=600 and different AOT(865) values.
28
For the minimum SNR(NIR) value of 600 suggested above, Figure 2.4 shows that for the three GSM
retrievals the errors become stable in the 800-1000 SNR(vis) range (Chl gets stable at higher SNRs than
the other 2 retrievals). The mean error (for the full range of Chl values used as input into MM01) remains
under 10% for the clear atmosphere cases only (AOT(865) 0.1). This is confirmed in Figure 2.3 where
the errors in the GSM retrievals stay under or close to 10% (except for CDM in eutrophic waters) for
clear atmospheres and high SNRs. For the visible bands, a minimum SNR of ~1000 is thus recommended.
2.4. Measurement requirements summary The ocean radiometer requirements are outlined in the following two tables. The first provides general
sensor performance and mission support requirements. The second lists specific data on multispectral
bands, bandwidths, typical clear sky top-of-atmosphere radiances over the ocean, saturation radiances,
and minimum SNRs (based on the analyses above, Table 2.3). In Table 2.2, the SNR value at 350 nm is
lower than in the other UV bands because its application for detecting absorbing aerosols does not require
a value of 1000. Also, the SNR at 678 nm is set at 1400 based on analysis of MODIS retrievals (the bio-
optical sensitivity analyses above did not include fluorescence line height). In the wavelength domain of
345-755 nm, multispectral bands are aggregations of 5 nm hyperspectral bands.
Table 2.2. General requirements for ocean radiometer and mission support
Radiometer Spectral Attributes
26 Spectral Bands (see Table 3)
10 nm fluorescence bands (667, 678, 710, 748 nm band centers)
10 to 40 nm bandwidth aerosol correction bands at 748, 765, 865, 1245, 1640, 2135 nm
820 nm band for estimation of column water vapor concentration
350 nm band for absorbing aerosol detection
5 nm resolution 345 to 755
Functional group derivative analyses
Polarization ≤ 1% sensor radiometric sensitivity, ≤ 0.2% prelaunch characterization accuracy
No saturation in multispectral bands
Accuracy & Stability Prelaunch radiance calibration accuracy
≤ 2%
On-orbit vicarious calibration accuracy
≤ 0.2%
Radiometric stability knowledge
≤ 0.1%, mission duration ≤ 0.1%, one-month prelaunch verification
Spatial Coverage 2-day global coverage ±58.3° cross-track swath
1 km resolution at center of swath
Other ±20° sensor tilt Sunglint avoidance
5 year minimum design lifetime
Monthly lunar imaging 7° phase angle through Earth-view sensor port
29
Table 2.3. OES multispectral band centers, bandwidths (), typical top-of-atmosphere clear sky ocean radiances
(Ltyp), saturation radiances (Lmax), and minimum SNRs at Ltyp. Radiance units are mW cm−2 μm−1 sr−1.
Ltyp Lmax SNR-spec
350 15 7.46 35.6 300
360 15 7.22 37.6 1000
385 15 6.11 38.1 1000
412 15 7.86 60.2 1000
425 15 6.95 58.5 1000
443 15 7.02 66.4 1000
460 15 6.83 72.4 1000
475 15 6.19 72.2 1000
490 15 5.31 68.6 1000
510 15 4.58 66.3 1000
532 15 3.92 65.1 1000
555 15 3.39 64.3 1000
583 15 2.81 62.4 1000
617 15 2.19 58.2 1000
640 10 1.90 56.4 1000
655 15 1.67 53.5 1000
665 10 1.60 53.6 1000
678 10 1.45 51.9 1400
710 15 1.19 48.9 1000
748 10 0.93 44.7 600
765 40 0.83 43.0 600
820 15 0.59 39.3 600
865 40 0.45 33.3 600
1245 20 0.088 15.8 250
1640 40 0.029 8.2 180
2135 50 0.008 2.2 100
30
GLOSSARY OF TERMS
a Total Absorption Coefficient
aCDOM Colored Dissolved Organic Material Absorption Coefficient
ACE Aerosol-Cloud-Ecosystem Mission
ad Detrital Absorption Coefficient
AOT Aerosol Optical Thickness
aph Phytoplankton Absorption Coefficient
bb Backscattering Coefficient
bbp Particulate Backscattering Coefficient
C Carbon
c Attenuation coefficient
CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
CDM Colored Dissolved and Detrital Material, see Maritorena et al. [2002]
CDOM Colored Dissolved Organic Material
Chl Chlorophyll
CLIVAR World Climate Research Program Climate Variability
Cphyto Phytoplankton Carbon Concentration
CZCS Coastal Zone Color Scanner
DIC Dissolved Innorganic Carbon
DOC Dissolved Organic Carbon
Envisat ESA Earth Observing Satelite
FQY Fluorescence Quantum Yield
FLH Normalized Fluorescence Line-height
GOCECP Global Ocean Carbon, Ecosystems, and Coastal Processes
GSFC NASA Goddard Space Flight Center
GSM01 Semi-Analytical Ocean Color Model, Maritorena et al. [2002]
HAB Harmful Algal Bloom
Kd(490) Diffuse Attenuation Coefficient for Downwelling plane irradiance at 490 nm
Lmax Top of Atmosphere Maximum Radiances
Ltyp Top of Atmosphere Typical Radiances
M80 Maritime Aerosol Model
MERIS Medium Resolution Imaging Spectrometer
ML Mixed Layer
MLD Mixed Layer Depth
MM01 chlorophyll-based optical model, Morel and Maritorena [2001]
MODIS Moderate Resolution Imaging Spectroradiometer
NESDS National Research Council Earth Sciences Decadal Survey
NIR Near Infrared
Bandwidth
NPP Net Primary Production
OCB Ocean Carbon and Biogeochemistry
OCEaNS Ocean Carbon, Ecology and Near-Shore
31
OES Ocean Ecosystem Spectroradiometer
OOI Ocean Observatories Initiative
PACE Plankton, Aerosol, Cloud, ocean Ecosystem
PAR Incident Photosynthetically Available Radiation
PhyLM Physiology-Multispectral Mission
PIC Particulate Inorganic Carbon
POC Particulate Organic Carbon
PSD Particle Size Distribution
[w()]N Normalized Water-Leaving Reflectance
rn Unique random number
Rrs(l) Remote Sensing Reflectance
Rrs_MM01 (λ, Chl) Remote Sensing Reflectance derived following Morel and Maritorena [2001]
Rrs_NIR(λ) Remote Sensing Reflectance caused by the errors in NIR bands
Rrs_TOA(λ) Atmospheric component contributing to the Top of Atmosphere
SCIAMACHY Scanning Imaging Absorption SpectroMeter for Atmospheric CHartographY
SeaBASS SeaWiFS Bio-optical Archive and Storage System
SeaWiFS Sea-Viewing Wide Field of View Sensor
SLC Synechococcus-like cyanobacteria
SNR Signal-to-Noise Ratio
SNR-spec Minimum Signal To Noise Ratio at Top of Atmosphere typical Radiances
SOLAS Surface Ocean-Lower Atmosphere Study
SQ Science Question
SSH Sea Surface Height
SST Sea Surface Temperature
STD Standard Deviation
SWIR Shortwave Infrared
T80 Tropospheric Model
TChl-a Total Chlorophyll-a concentration
TOA Top Of Atmosphere
UV Ultra-Violet
VIIRS Visible Infrared Imaging Spectroradiometer
Z1% 1% Photosynthetically Available Radiation Depth
%rms Root Mean Square, expressed as rms*100/reference
32
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